Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Optimizing Simple Tabular Reduction with a Bitwise Representation
Authors: Ruiwei Wang, Wei Xia, Roland H. C. Yap, Zhanshan Li
IJCAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental evaluation show our algorithms are faster than many algorithms (STR2, STR2-C, STR3, STR3-C and MDDc) across a variety of benchmarks except for problems with small tables where complex data structures do not payoff. |
| Researcher Affiliation | Academia | 1School of Software, Jilin University, Changchun, China 2Key Laboratory of Symbol Computation and Knowledge Engineering, Education Ministry, China 3School of Computing, National University of Singapore, Republic of Singapore |
| Pseudocode | Yes | Algorithm 1: STRbit (C : Constraint) |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We consider classical series instances1, the series2 introduced in STR2-C, and the PH-k-j series used in STR3 (896 instances). Footnotes point to http://www.cril.univ-artois.fr/%7Elecoutre/benchmarks.html and http://www.comp.nus.edu.sg/%7Exiawei/STRC-benchmarks/, which are public benchmark repositories. |
| Dataset Splits | No | The paper mentions using benchmarks but does not specify explicit training, validation, or test dataset splits, nor does it describe a cross-validation setup. |
| Hardware Specification | Yes | Experiments are run on a 3.40 GHz Intel core i7 processing on Linux |
| Software Dependencies | No | Our implementation uses 64-bit numbers (long Java type) so w = 64 and partitions the table in lexico order. In addition, we extract the c-table from the MDD. Experiments are run on a 3.40 GHz Intel core i7 processing on Linux, and all algorithms use the dom/ddeg variable ordering heuristic and lexico value ordering heuristic. Timeout is 600 seconds. While Java is mentioned, no specific version is given. The solver Abscon is mentioned with a citation, but no version number. |
| Experiment Setup | Yes | all algorithms use the dom/ddeg variable ordering heuristic and lexico value ordering heuristic. Timeout is 600 seconds. |